VIII Abiotic Stress Response

Genomics Approaches to Dissect the Genetic Basis of Drought Resistance in Durum Wheat

Abstract A better knowledge of the genetic basis of the mechanisms underlying the adaptive response to drought will be instrumental to more effectively deploy marker-assisted selection (MAS) to improve yield potential while optimizing wateruse efficiency. Genomics approaches allow us to identify and clone the genes and QTLs that underlie the adaptive response of durum wheat to drought. Linkage and association mapping have allowed us to identify QTLs for traits that influence drought resistance and yield in durum and bread wheat. Once major genes and QTLs that affect yield under drought conditions are identified, their cloning provides a more direct path for mining and manipulating beneficial alleles. While QTL analysis and cloning addressing natural variation will increasingly shed light on mechanisms of adaptation to drought and other adverse conditions, more emphasis on approaches relying on resequencing, candidate gene identification, 'omics' platforms and reverse genetics will accelerate the pace of gene/QTL discovery. Genomic selection provides a valuable option to improve wheat performance under drought conditions without prior knowledge of the relevant QTLs. Modeling crop growth and yield based on the effects of major QTLs offers an additional opportunity to leverage genomics information. Although it is expected that genomics-assisted breeding will enhance the pace of durum wheat improvement, major limiting factors are how to (i) phenotype genetic materials in an accurate, relevant and highthroughput fashion and (ii) more effectively translate the deluge of molecular and phenotypic data into improved cultivars. A multidisciplinary effort will be instrumental to meet these challenges.

Keywords Consensus map • Drought • Genomics • Durum wheat • Marker-assisted selection • Modeling • Phenotyping • QTL • Roots • Yield

Introduction

Among all abiotic stresses most affected by climate change, drought is the major one curtailing global wheat production. Although conventional breeding has steadily increased crop productivity under drought conditions and across a broad range of environmental constraints, the present rate of increase in wheat productivity is insufficient to meet food security globally. Genomics-assisted crop improvement provides novel opportunities to enhance the yearly rate of increase in wheat yield while advancing our understanding of the genetic and functional basis of the adaptive response to water-limited conditions (Habash et al. 2009; Fleury et al. 2010; Able and Atienza 2014; Tuberosa et al. 2014). This chapter illustrates how genomics approaches have been applied to genetically dissect durum wheat performance under water-scarce conditions and, more in general, how this information might help to mitigate the negative effects of drought on wheat productivity. In view of the genetic and functional similarity shared by durum and bread wheat, a number of relevant examples for the latter have also been considered.

Dissecting the Genetic Basis of Drought Resistance in Durum Wheat

The genetic basis of grain yield and the morpho-physiological traits that determine durum wheat performance under drought involves a myriad of quantitative trait loci (QTLs) of widely different effects, mostly too small to be detected experimentally. One of the reasons accounting for the modest impact of genomics-assisted breeding on the release of drought-tolerant cultivars is that screening conditions adopted under controlled conditions (e.g. growth chamber) usually provide a rather poor surrogate of the dynamics of the drought episodes that crops are exposed in the field (Passioura 2007; Tuberosa 2012). Additionally, the high context-dependency of QTL effects according to the genetic background and environment (Maphosa et al. 2014) further limits the effectiveness of marker-assisted selection (MAS) for improving field performance under drought conditions.

Until the introduction of association mapping, QTL identification has been pursued via linkage mapping based on the evaluation of biparental populations of recombinant inbred lines (RILs). Notably, the availability of maps obtained with different crosses and sharing common polymorphisms allows for the construction of a consensus map (Maccaferri et al. 2014) that in turn enables an even more accurate comparative analysis (e.g. meta-analysis) of QTL positions.

Association mapping (AM) based on sets of unrelated accessions provides additional opportunities to identify the loci (genes and/or QTLs) for target traits. In durum wheat, the evaluation of a panel of elite accessions characterized by high LD (>1 cM) has allowed for a genome-wide search using a limited number of markers (Maccaferri et al. 2005, 2011). Although AM has mostly targeted traits with a genetic basis less complex than drought tolerance (e.g. resistance to biotic stress), some studies have targeted drought-adaptative traits and grain yield in durum wheat grown under varying water regimes (Sanguineti et al. 2007; Maccaferri et al. 2011; Canè et al. 2014; Graziani et al. 2014).

Notwithstanding the clear advantages of AM as compared to biparental linkage mapping, a major limitation of the former is the high rate of false positives (i.e. Type-I error rate) due to the presence of hidden population structure. Additionally, for highly integrative and functionally complex traits such as yield, particularly under drought conditions, the effectiveness of AM is reduced by the fact that different genotypes may show similar phenotypes due to trait compensation (e.g. yield components), which inevitably undermines the identification of significant markertrait association. Although the number of studies is clearly too limited to draw more certain conclusions on the validity of AM in identifying QTLs for yield, the results reported by Maccaferri et al. (2008, 2011) in durum wheat suggest that the identification of yield QTLs under different water regimes should also be pursued via biparental mapping. This is particularly true whenever the investigated trait (e.g. yield under drought conditions) is strongly influenced by flowering time, in which case the overwhelming effects on yield of this phenological covariate will overshadow the effects due to the action of yield per se QTLs, hence reducing the possibility of identifying them.

 
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